Explore AI Agent Skills & Claude Prompts
Discover open-source agent skills for Claude Code, Codex, ChatGPT, and any tool that uses SKILL.md.
Enter through keywords, occupations, creators, and GitHub sources to see what kinds of skills are emerging across domains.
Use the same catalog through the API
Connect 381,784 public skills to your own search, analytics, or agent workflow with the REST API.
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run-parallel-agents-feature-build
by ulpi-ioAutomatically orchestrate multiple specialized agents working in parallel when building independent features, modules, or performing separate investigations. Use when the task list contains 3+ unrelated features/tasks that don't share state, don't have execution dependencies, and can be understood independently. Match each feature to the right expert agent (Laravel, Next.js, React, Node, NestJS, Remix, Express, Expo, Flutter, Magento) and run them concurrently to maximize development speed.
run-parallel-agents-feature-debug
by ulpi-ioAutomatically orchestrate multiple specialized agents working in parallel to debug, diagnose, and fix independent problems across different subsystems. Use when encountering 3+ unrelated bugs, test failures, or issues in isolated modules that don't share root causes. Match each problem to the right expert agent (Laravel, Next.js, React, Node, NestJS, Remix, Express, Expo, Flutter, Magento) to diagnose and resolve issues concurrently, maximizing debugging efficiency.
rust
by ulpi-ioRust systems programming — storage engines, binary formats, SIMD, wire protocols, DataFusion/Arrow, tantivy search, HNSW vectors, arena allocation, graph engines, R-tree geo, async tokio, mmap, MVCC, zero-copy, io_uring. Use when working on any Rust crate involving database internals, storage, query execution, network protocols, search, vectors, or high-performance data structures.
cost-estimate
by ulpi-ioEstimate development cost of a codebase (full repo, branch diff, or single commit). Invoke via /cost-estimate or when user says "estimate cost", "how much would this cost", "development cost". Accepts optional scope args like "branch:feat/foo" or "commit:abc1234".
ast-grep
by ulpi-ioStructural code search via AST patterns. Find code by structure, not text — async functions without error handling, specific API call patterns, missing guards. Use when grep/ripgrep can't express what you need. Requires ast-grep CLI.
ast-grep
by ulpi-ioGuide for writing ast-grep rules to perform structural code search and analysis. Use when users need to search codebases using Abstract Syntax Tree (AST) patterns, find specific code structures, or perform complex code queries that go beyond simple text search. This skill should be used when users ask to search for code patterns, find specific language constructs, or locate code with particular structural characteristics.
run-parallel-agents-feature-debug
by ulpi-ioAutomatically orchestrate multiple specialized agents working in parallel to debug, diagnose, and fix independent problems across different subsystems. Use when encountering 3+ unrelated bugs, test failures, or issues in isolated modules that don't share root causes. Match each problem to the right expert agent (Laravel, Next.js, React, Node, NestJS, Remix, Express, Expo, Flutter, Magento) to diagnose and resolve issues concurrently, maximizing debugging efficiency.
run-parallel-agents-feature-build
by ulpi-ioAutomatically orchestrate multiple specialized agents working in parallel when building independent features, modules, or performing separate investigations. Use when the task list contains 3+ unrelated features/tasks that don't share state, don't have execution dependencies, and can be understood independently. Match each feature to the right expert agent (Laravel, Next.js, React, Node, NestJS, Remix, Express, Expo, Flutter, Magento) and run them concurrently to maximize development speed.
vhs
by ulpi-ioVHS terminal recording best practices from Charmbracelet (formerly charmbracelet-vhs). This skill should be used when writing, reviewing, or editing VHS tape files to create professional terminal GIFs and videos. Triggers on tasks involving .tape files, VHS configuration, terminal recording, demo creation, or CLI documentation.
microsoft-foundry
by ulpi-ioDeploy, evaluate, and manage Foundry agents end-to-end: Docker build, ACR push, hosted/prompt agent create, container start, batch eval, prompt optimization, agent.yaml, dataset curation from traces. USE FOR: deploy agent to Foundry, hosted agent, create agent, invoke agent, evaluate agent, run batch eval, optimize prompt, deploy model, Foundry project, RBAC, role assignment, permissions, quota, capacity, region, troubleshoot agent, deployment failure, create dataset from traces, dataset versioning, eval trending, create AI Services, Cognitive Services, create Foundry resource, provision resource, knowledge index, agent monitoring, customize deployment, onboard, availability, standard agent setup, capability host. DO NOT USE FOR: Azure Functions, App Service, general Azure deploy (use azure-deploy), general Azure prep (use azure-prepare).
community-forum
by ulpi-ioWhen the user wants to promote via forums, communities, or invite users to join a community. Also use when the user mentions "forum promotion," "Indie Hacker," "Hacker News," "community growth," "Discord promotion," "vertical community," "brand encyclopedia," "Wikipedia," "Quora," "Reddit community," "community building," "forum marketing," or "community invite."
memory-management
by ulpi-ioThis skill should be used when the user asks to "remember project context", "save SEO data", "track campaign progress", "store keyword data", "manage project memory", "remember this for next time", "save my keyword data", or "keep track of this campaign". Manages a two-layer memory system (hot cache + cold storage) for SEO/GEO project context, tracking keywords, competitors, metrics, and campaign status with intelligent promotion/demotion.
Browse Agent Skills by Occupation
23 major groups · 867 SOC occupations
Browse by Category
Explore agent skills organized by their primary use case
Explore the agent skills ecosystem by occupation and creator
SkillMD is not just a keyword search box. It is an open map that organizes public skills by occupation, creator, and repository, helping you see which workflows, judgment criteria, and domain habits people are writing for AI agents.
Then follow creators and GitHub repositories back to the source: compare the skills a team maintains, whether the repo is active, and how the README frames the work before you open, install, or reuse anything.
Use it three ways: learn an unfamiliar field by occupation, study how creators organize skills, then use source context to decide what is worth opening or reusing.
01 Map a field
Browse 23 occupation groups and 867 SOC roles to learn what skills exist in adjacent domains and how they break down real work.
02 Follow creators
Use creator and repository pages to inspect maintained skill collections, recent updates, and source context before trusting a result.
03 Search with sources
Search 1.7M+ collected skills, then use occupation tags, creators, and GitHub source context to decide what is worth opening.
Start with the occupation map, then follow creators and repositories back to real code. SkillMD helps explain why a skill is worth opening, not only what it is named.
Standardizing Agent Capabilities with SKILL.md and Model Context Protocol (MCP)
In the rapidly evolving landscape of artificial intelligence, LLM agents (Large Language Model agents) have transitioned from simple text predictors to autonomous problem solvers. To orchestrate complex, multi-step agentic workflows, developers require a standardized format to specify agent capabilities, prompt instructions, system rules, and database bindings. This is where SKILL.md and the Model Context Protocol (MCP) have emerged as standard developer paradigms. SkillMD serves as the central directory for indexing, exploring, and sharing these critical agent configurations.
Our open-source registry currently tracks over 1.7 million collected SKILL.md configurations and system prompts. By compiling agent configurations from active developers on GitHub, we bridge the gap between prompt engineering research and production execution. Whether you are building agents with Anthropic's Claude Code, OpenAI's GPT-4, Google's Gemini, or local models using Ollama and LlamaIndex, standardized skill definitions ensure your agents behave predictably across different runtime environments.
What is the Model Context Protocol (MCP)?
The Model Context Protocol (MCP) is an open-source standard designed to connect LLMs to data sources, developer tools, and external environments. MCP establishes a bidirectional communication channel between client applications (like Cursor, Claude Desktop, or custom agent systems) and servers hosting data or capabilities. Standardizing instructions via SKILL.md enables LLMs to query databases, read local files, execute terminal commands, and integrate third-party APIs. SkillMD allows you to find ready-to-run MCP servers and prompt instructions for various occupations and technical tasks.
The Structure of a Professional SKILL.md File
A valid SKILL.md configuration is designed to be easily read by humans and parsed by LLMs. It contains precise system instructions, trigger conditions, required parameters, and execution examples. Below is the typical architectural blueprint of a professional agent skill:
- Metadata & Core Scope: Declares the name of the skill, author details, target models, and a description of the capability.
- Triggers & Intent Detection: Details semantic triggers that help the agent decide when to invoke this skill.
- System Prompts: Explicit system-level instructions that direct the agent's behavior, personality, safety guardrails, and formatting preferences.
- Capabilities & Tools: Lists the files, databases, or APIs the agent must access to complete the tasks.
- Few-Shot Examples: Demonstrates real inputs and outputs, helping the model generalize behavior through in-context learning.
Optimizing Agent Workflows for Modern LLMs
Writing effective agent skills requires deep knowledge of prompt engineering. With the release of advanced reasoning models like Claude 3.5 Sonnet, ChatGPT o1, and DeepSeek-V3, prompt templates must focus on structured thinking. Developers are encouraged to use XML tags (e.g., <thought>, <context>, and <rules>) to isolate execution boundaries. Standardized prompts prevent agents from suffering from context drift, ensuring that long-running tasks remain aligned with the initial system parameters.
Exploring by SOC Occupations and Creator Profiles
What makes SkillMD unique is its taxonomy. Instead of simple text search, we parse and organize files according to the Standard Occupational Classification (SOC) system. This means you can discover skills written for Computer and Mathematical roles, Business and Financial operations, Legal, Design, and and Educational Instruction fields. By tracking creator profiles, developers can study how different teams organize their custom instructions, compare version updates, and fork public configs for specialized enterprise use cases.
SkillMD operates as a high-performance index running on a fast Go backend and a highly responsive Astro SSR frontend. All search queries execute in milliseconds, featuring smart debouncing to prevent multiple API requests while keeping user data secure. Join our community of developers to standardize your AI agent instructions and optimize your LLM prompting workflows today.
Frequently Asked Questions
A practical guide to agent skills: what they are, how to inspect them, and how SkillMD helps you explore the ecosystem.